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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'validation' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
signal_sd: double
reps: int64
mean_r2_vs_hist: double
reject_vs_hist: double
mean_r2_vs_shrink: double
reject_vs_shrink: double
vs
signal_sd: double
seed: int64
r2_vs_hist_pct: double
cw_t_hist: double
r2_vs_shrink_pct: double
cw_t_shrink: double
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 606, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5012, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              signal_sd: double
              reps: int64
              mean_r2_vs_hist: double
              reject_vs_hist: double
              mean_r2_vs_shrink: double
              reject_vs_shrink: double
              vs
              signal_sd: double
              seed: int64
              r2_vs_hist_pct: double
              cw_t_hist: double
              r2_vs_shrink_pct: double
              cw_t_shrink: double

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Open Economic & Quant Research Data

Versioned research data for CasualLab, Macroeconomics, Mortgage Rate Lock-In and Housing Market Dynamics, Tariff Incidence, Order Flow to Price Impact, Emerging-Market Factor Timing, Polymarket Cross-Sectional Factor Study, and Crypto Factor Zoo and Post-Publication Audit, including publishable data, fixtures, reports, and reproducibility documentation.

Project code, tests, and run configuration live in the GitHub repositories listed under Related resources. dataset_manifest.json describes the 8-project release.

Repository structure

  • CasualLab/: causal inference and policy-simulation research content.
  • Macroeconomics/: vintage-aware forecasting and public-source adapter research content.
  • RealEstate/: housing-finance research on mortgage lock-in, mortgage exits, local activity, prices, and construction. Registered loan-level records and loan-granular derivatives are excluded.
  • TariffIncidence/: official-data research on U.S. Section 301 tariff pass-through, sourcing reallocation, and domestic input-output propagation. Large raw, intermediate, analytical, and parquet result files are excluded from the public package.
  • Microstructure/: research-only market-microstructure protocols, reports, and documentation. Exchange observations, normalized or derived tables, fitted states, ingestion authorities, and generated run bundles are excluded.
  • EMFactorTiming/: emerging-market factor timing out-of-sample evaluation tables, JKP coverage metadata, and shrinkage benchmark reports across 21 emerging and 23 developed markets.
  • PolymarketFactors/: binary contract artifact funnel tables, price-neutralized factor IC summaries, category reversal diagnostics, and sample market fixtures.
  • CryptoFactors/: equity-to-perpetual factor transfer tables, a Benjamini-Hochberg FDR demonstration on a 10-date sample fixture, post-publication out-of-sample decay diagnostics, and sample perpetual panel fixtures. Published long-short tables charged about 2.5 bp per side instead of the stated 5 bp; see the project README's cost erratum.
  • dataset_manifest.json: file counts, exclusions, and release metadata for the 8-project release.

Supported uses

Reproducibility review, economics and quantitative-method research, public fixture exploration, and development of documented derivatives. The repository is not a single homogeneous machine-learning table; inspect each project README and schema before loading files.

Sources and processing

Each project retains its own provenance notes. Raw third-party sources remain subject to provider terms. Local Python environments, package installations, caches, bytecode, and operating-system metadata are excluded because they are not research data.

Licensing

Project-owned fixtures and documentation follow project-local terms; code terms are in each GitHub repository. Third-party data and dependencies retain their original terms. Inclusion in this versioned mirror does not grant a new license. Assets without confirmed redistribution terms remain marked RIGHTS_UNKNOWN in the observatory governance records.

Limitations

  • File types and schemas vary by project.
  • Some raw source rights require independent review.
  • Updated numerical comparisons are published only with benchmark and validation evidence.
  • Use a pinned Dataset revision for reproducible analysis.

Related resources

Citation

Cite the observatory, this Dataset revision, the relevant project, and every original data provider used in an analysis.

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